Exploring the role of the social vulnerability index in understanding COVID-19 immunization rates

Communities that are historically marginalized and minoritized were disproportionately impacted by the COVID-19 pandemic due to long-standing social inequities. It was found that those who experience social vulnerabilities faced a heightened burden of COVID-19 morbidities and mortalities and concerningly lower rates of COVID-19 vaccination. The CDC’s Social Vulnerability Index (CDC-SVI) is a pivotal tool for planning responses to health crises such as the COVID-19 pandemic. This study explores the associations between CDC-SVI and its corresponding themes with COVID-19 vaccine uptake in Nevada counties. Additionally, the study discusses the utility of the CDC-SVI in the context of equitable vaccine uptake in a pandemic setting. We examined the linear association between the 2020 CDC-SVI (including the composite score and the four themes) and COVID-19 vaccine uptake (including initial and complete vaccinations) for the seventeen Nevada counties. These associations were further examined for spatial-varied effects. Each CDC-SVI theme was negatively correlated with initial and complete COVID-19 vaccine uptake (crude) except for minority status, which was positively correlated. However, all correlations were found to be weak. Excessive vaccination rates among some counties are not explained by the CDC-SVI. Overall, these findings suggest the CDC-SVI themes are a better predictor of COVID-19 vaccine uptake than the composite SVI score at the county level. Our findings are consistent with similar studies. The CDC-SVI is a useful measure for public health preparedness, but with limitations. Further understanding is needed of which measures of social vulnerability impact health outcomes.


Introduction
Communities that have been historically and intentionally excluded were disproportionately burdened by the COVID-19 pandemic [1,2].These communities consist of those historically

Study area
The population of Nevada is comprised of 3.1 million people across 17 counties (Fig 1) [22].The state is the sixth fastest-growing and is among the nation's top ten most racially and ethnically diverse [23,24].Among Nevada's counties, Clark County is the most populated (2.3 million), making up almost 73% of the state's total population count, and is the 22 nd most diverse county in the U.S. [22,25,26].

Data collection
To examine the relationship between COVID-19 vaccine coverage and social vulnerability, we collected vaccine coverage data by county (n = 17) from the Nevada Department of Health and Human Services.The data was gathered on a weekly basis from April 19, 2021, to April 5, 2022.
The latest CDC-SVI data in 2020 at the county level in Nevada was computed from 16 social factors grouped into four themes, according to the CDC-SVI methods (Table 1) [27].The raw data were gathered from the 2016-2020 5-year estimates from the American Community Survey (ACS), which were transformed into percentile percentages and summed for each theme.The sums of the themes were then combined to form the CDC-SVI score with equal weight to each theme.Counties with a higher score are considered to have higher vulnerability.

Data analysis
This study first examined the linear association between CDC-SVI (including CDC-SVI itself and the four themes) and COVID-19 initial and complete vaccinations, adjusted by spatial effects.We defined initial vaccine coverage as the cumulative percentage of the population who started a COVID-19 vaccine series (received only a first dose of either Pfizer-BioNTech or Moderna vaccine), not including COVID-19 boosters.Complete vaccination coverage was defined as the cumulative percentage of the population who completed a COVID-19 vaccine series (received two doses of Pfizer-BioNTech or Moderna, or one dose of Johnson & Johnson/Janssen vaccine), not including COVID-19 boosters.Then, we further examined the spatial-varied effects between CDC-SVI and COVID-19 vaccination to identify areas with a high CDC-SVI leading to a high vaccination rate.Because these analyses involved spatial functions, which cannot be well adopted in traditional models, we instead applied the Bayesian additive model to fulfill our study aims [28].By defining Y it as the number of people with initial or complete vaccination in the i th county (i = 1, . .., 17) on the t th week (t = 1, . .., 53), it follows a Poisson distribution with a mean parameter μ it , which can be expressed as: Model 1: where α is the intercept, and β j and β are the coefficients of the j th theme and CDC-SVI itself.The term f(t) is a nonlinear function with a penalized smoothing basis and 6 interior knots for the calendar time t.The term f(spatial) is a spatial function estimated by Markov random fields, which derived a neighborhood matrix to estimate the spatial coefficient [29].When the spatial function is an independent term in the model, like Model 1, the spatial estimate can be explained as the excessive rate of COVID-19 vaccination unexplained by the themes and CDC-SVI itself.The last term is an offset from the logarithm of the 2020 population at the county level.
To further investigate the association between the themes or CDC-SVI and vaccination rate varied by county, we replaced linear terms with interaction terms between each theme or CDC-SVI and the spatial function in Model 1 and Model 2, expressed as: Model 3: All model settings in Model 3 and Model 4 are identical to those in Model 1 and Model 2. All unknown parameters were estimated through a Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations.The posterior distribution of each unknown parameter was first built by 50,000 iterations, where the first 10,000 iterations were burn-in, and a sample was drawn every 40 iterations.The mean of the posterior distribution represented the point estimate of an unknown parameter.Based on the significance level of 0.05, the 2.5 th and 97.5 th percentiles of the posterior distribution composed the 95% credible interval (CI) of a point estimate.Thus, a significant estimate was determined by its 95% CI strictly larger or smaller than 0. We also utilized the deviance information criterion (DIC) to compare models.
The estimated coefficients of linear predictors or spatial estimates are equivalent to the logarithm of relative risk (logRR), which was transformed into RR% to facilitate epidemiological explanations.The 95% CI of RR% has also transformed accordingly.All models were diagnosed through worm plots, trace plots, and autocorrelation function plots for accessing the fit of our models, the robustness of resampling in MCMC, and the autocorrelation, respectively.Sensitivity analysis was conducted with another two sets of hyperparameters in terms of (1, 0.05) and (0.001, 0.001).We applied the repeated measures ANOVA to compare them with the original results.
Data were cleaned and summarized by SAS v9.4 (SAS Institute Inc., Cary, NC).Spatial data analysis and mapping were accomplished by RStudio v2022.07.2+576 (RStudio Team, Boston, MA).The significant level was set to 5%.  2 shows that among the four themes, the highest average was 2.49 (SD = 0.94) in the socioeconomic status theme, whereas the minority theme has the lowest average of 0.05 (SD = 0.32).The theme proportions further reveal that, on average, the housing characteristics theme has the largest proportion representing 32.38% (SD = 7.35) in the SVI.In contrast, the minority theme has the smallest proportion with an average of 5.97% (SD = 3.29) in the CDC-SVI.We also found that the highest theme score appeared differently in different counties, implying that a theme with a higher proportion still displays an equal role to a theme with a lower proportion because the CDC-SVI was computed by an immediate summation.

Vaccinations and CDC-SVI by county
Table 3 shows the correlation coefficients among vaccination, CDC-SVI, and its themes.The CDC-SVI was negatively correlated with both vaccination rates.Among the four themes, socioeconomic status, household characteristics, and housing and transportation themes were negatively correlated with both vaccination rates.Minority status is the only theme positively correlated with both vaccination rates.All correlations between vaccination rates and themes were weak.The highest correlation among the four themes was 0.57 (95% CI = 0.10, 0.82) between socioeconomic status and household characteristics.The CDC-SVI itself is highly correlated with the socioeconomic status theme by 0.82 (95% CI = 0.54, 0.93).

Measuring linear associations between CDC-SVI, themes, and vaccinations for the state
Table 4 shows the linear relationship between themes and vaccination from the first two models.In Model 1, only the minority status theme was significantly associated with complete vaccination with an RR% of 71.68 (95% CI = 1.46, 180.82), but no significant association was found for initial vaccination.In Model 2, the CDC-SVI was not significantly related to both initial and complete vaccinations.Regardless of initial vaccination or complete vaccination,  Model 1 has a smaller DIC than Model 2, indicating that the four themes performed better than the CDC-SVI.Fig 3 shows the spatial pattern with respect to the estimated spatial function in Model 1 and Model 2, revealing that, even though those patterns looked similar between the two models in either initial or complete vaccination, Model 2 had more significant counties than Model 1. Fig 3 also reveals that the vaccination rate was not completely explained by themes as linear terms in the first two models.More counties have either positively or negatively significant spatial estimates in Model 2 than in Model 1, indicating their initial and complete vaccination rates were excessively higher or lower than the state average.Overall findings indicate that the themes predict initial and complete vaccination rates better than the CDC-SVI itself, but the only significant spatial estimate was the minority status theme.In addition, positively significant counties more likely appeared in southern and western Nevada, indicating that these areas had excessive vaccination rates not explained by the CDC-SVI and its four themes.

Measuring interactions between CDC-SVI, themes, and vaccinations by county
When interacting with the spatial function in Model 3, the four themes display very different patterns, revealing that their influence on COVID-19 vaccination varied by county.Fig 4a shows that the RR% for initial vaccination was positive in all counties with respect to household characteristics and minority status themes, indicating that higher scores of household characteristics and minority status lead to the increase in COVID-19 vaccination rates in each county.On the contrary, socioeconomic status and housing and transportation themes were negatively associated with COVID-19 vaccination in all counties.However, none of them were statistically significant.Similarly, Fi 4b shows that the association between each theme and COVID-19 vaccination varied by county, but none were statistically significant.Compared to Model 1, where the four themes were linear predictors, Model 3 had worse performance with a much higher DIC for both initial vaccination (14059338 vs. 14139654) and complete vaccination (13423872 vs. 13454926).When the CDC-SVI itself interacted with the spatial function in Model 4, 16 of 17 counties had a negative RR% for initial vaccination, including three significant findings in Eureka County, Lincoln County, and Pershing County (Fig 5a).For complete vaccination, 12 of 17 counties had a negative RR%, whereas only Lincoln County had a significant association (Fig 5b).Compared to Model 2, where the CDC-SVI was defined as a linear predictor, Model 4 had a worse performance with a much higher DIC for initial vaccination

Discussion
The COVID-19 pandemic exposed and exacerbated deep-rooted health and social inequities worldwide, taking a disproportionately heavy toll on communities that are under-resourced.Therefore, developing vaccine equity and resource allocation strategies to identify communities that are historically socially vulnerable could not have been more urgent.This study was performed to determine how well the CDC-SVI can be used to explain vaccine uptake.The findings from our study indicate that the total CDC-SVI score was not a good predictor of COVID-19 incomplete or complete vaccinations, but certain themes may be better predictors at the county level.
To tackle disparities and the health impacts of emergencies, it is crucial to address the social determinants that contribute to poor health outcomes.In recent studies, researchers have aimed to better understand the connections between vulnerability factors, social vulnerability indices, and outcomes related to disasters and emergencies.The findings from this study are consistent with similar studies that have examined these associations.For instance, Karaye and  Horney [4] examined the association between CDC-SVI and COVID-19 case counts and found that "SVI variables explained only 38.9% (R 2 = 0.389) of the variability in COVID-19 case counts."Nayak et al. [5] found no significant association between the overall CDC-SVI and COVID-19 incidence, whereas COVID-19 case fatality rates were associated with socioeconomic status and minority status themes.Another study examining social vulnerability and influenza vaccinations among Medicare patients found a negative correlation between county SVI and influenza vaccine coverage [8].Notably, the impact of the CDC-SVI theme varied significantly depending on the geographical location.Thus, a theme that is a predictor in one area may not be the same in another.A distinction has been made in several studies examining the uneven distribution of social vulnerability in communities when responding to crises [30][31][32].In combination, these studies highlight the variability of influence each theme has in predicting outcomes by location and emphasize the importance of locally tailored interventions.
In our study, minority status was positively associated with incomplete and complete vaccinations in all 17 counties.This suggests that despite the proportion of minorities in a given county, the allocation of resources provided during the pandemic reached a diverse group of Nevadans.Resources included funding, pop-up clinics, mobile units, targeted and tailored messaging, and the use of various methods of communication to support vaccine uptake.On the other hand, housing type and transportation were found to negatively affect vaccination uptake in 15 counties, suggesting that barriers associated with housing and transportation may contribute to place-based disparities.However, statistically insignificant relationships or negative associations are harder to explain with the CDC-SVI and other similar methodologies.Rufat et al. [32] suggest that these relationships can result from the models being a poor indicator of social vulnerability processes, outcome measures that may not adequately reflect the social impacts, or unclearly defined conceptual relationships between social vulnerability and outcomes-further arguing the need for a stronger theoretical basis for selecting validation measures to assess social vulnerability.
Although the CDC-SVI was developed to support planning efforts during disaster relief, it has reliably found its way into broader public health efforts.It has partly done so because of the readily available data and ease of application [33].However, composite measures have their limitations.For instance, CDC-SVI, and similar indices that rely on census data, are indicator-based and provide a static measure [18,34].A static measure may not capture the changing dynamics of a community, such as changes to individuals, composition (e.g., demographics), and environment (e.g., development and resources) that occur between data collection periods [18,35].Considering the complicated and multidimensional nature of social vulnerability, future research should involve developing place-based indicators to account for variable-specific weights [36][37][38].Addressing the limitations of existing models may help narrow the gap in our understanding of social vulnerability factors and their potential influence [35].
While our analysis provided an overview of which components of the SVI may be better predictors to gauge vaccination rates, there are a few notable limitations.First, our data were aggregated at the county level and not at a finer area level (e.g., census tract), therefore not allowing us to take a more detailed view of the heterogeneity of the population, which would likely result in more variations [18].Second, the pandemic resulted in a flood of resources in response to the COVID-19 pandemic, which is not accounted for in the model and may have influenced vaccination rates overall.Lastly, and as noted above, the use of SVI has some limitations.Although it accounts for critical social and demographic factors, it is still a generalized index rather than a customized index specific to COVID-19 to capture all the potential influences on vaccination uptake.Since the SVI methodology is a composite index, it limits the ability to understand which aspects of social vulnerability are more important to vaccination.

Conclusion
As a result of the unfair and excessive impact on communities that have been made vulnerable, employing vaccine equity efforts is essential to mitigate the impact of long-standing inequities.The CDC-SVI was developed to provide emergency response planners, public health officials, and policymakers with a robust and comprehensive measure of a community's vulnerability to the impacts of external stressors such as natural disasters and public health emergencies.
Although it has its limitations, it certainly is valuable in helping mobilize prevention and mitigation efforts at the community level [34].
Achieving health equity is complex, and it requires adapting intervention models that consider the social, economic, and structural barriers to meet the needs of our communities [38].Of equal importance is examining or developing alternative or complementary methodologies that help us better capture the influence of social vulnerability factors and understand how specific determinants operate on a local level.As methodology and intervention models are adapted, we will be better equipped to support the development and implementation of public health efforts in communities that need them.Future research should consider the development of social vulnerability models that examine both static and dynamic variables that are weighted to better measure their influence.In addition, more work needs to be done to better understand the mechanism by which social vulnerability measures influence health outcomes.

Fig 3 .
Fig 3. Comparisons of relative rate percentages obtained from the spatial function in Model 1 and Model 2. Significance was determined by the 95% credible interval in each county.https://doi.org/10.1371/journal.pone.0302934.g003

Table 3 . Pearson correlations among crude vaccination rates and social vulnerability index themes.
Parentheses show the 95% confidence intervals.